{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 根据Pytorch迁移学习教程改编而得\n",
    "原文地址：\n",
    "\n",
    "http://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html\n",
    "\n",
    "使用迁移学习进行猫狗识别\n",
    "==========================\n",
    "\n",
    "这个教程将教你如何使用迁移学习训练你的网络. 你可以在 cs231n 笔记中阅读更多有关迁移学习的信息.\n",
    "\n",
    "http://cs231n.github.io/transfer-learning/\n",
    "\n",
    "引用自该笔记,\n",
    "\n",
    "    事实上, 很少有人从头(随机初始化)开始训练一个卷积网络, 因为拥有一个足够大的数据库是比较少见的. 替代的是, 通常会从一个大的数据集(例如 ImageNet, 包含120万的图片和1000个分类)预训练一个卷积网络, 然后将这个卷积网络作为初始化的网络, 或者是感兴趣任务的固定的特征提取器.\n",
    "\n",
    "如下是两种主要的迁移学习的使用场景:\n",
    "\n",
    "-  **微调卷积网络**: 取代随机初始化网络, 我们从一个预训练的网络初始化, 比如从 imagenet 1000 数据集预训练的网络. 其余的训练就像往常一样.\n",
    "-  **卷积网络作为固定的特征提取器**: 在这里, 我们固定网络中的所有权重, 最后的全连接层除外. 最后的全连接层被新的随机权重替换, 并且, 只有这一层是被训练的.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# License: BSD\n",
    "# Author: Sasank Chilamkurthy\n",
    "\n",
    "from __future__ import print_function, division\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "import copy\n",
    "\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Data\n",
    "---------\n",
    "数据集地址\n",
    "\n",
    "https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data\n",
    "\n",
    "# 比赛说明\n",
    "+ 在本次比赛中，您将编写一个算法来分类图像是否包含狗或猫。这对人类，狗和猫来说很容易。你的电脑会觉得有点困难\n",
    "\n",
    "## 特征说明\n",
    "+ Dogs vs. Cats是一个传统的二分类问题。\n",
    "    + 训练集包含25000张图片，命名格式为..jpg, 如cat.10000.jpg、dog.100.jpg\n",
    "    + 测试集包含12500张图片，命名为.jpg，如1000.jpg。\n",
    "+ 参赛者需根据训练集的图片训练模型，并在测试集上进行预测，输出它是狗的概率。\n",
    "+ 最后提交的csv文件如下，第一列是图片的，第二列是图片为狗的概率。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据目录结构为\n",
    "+ train\n",
    "  + dog\n",
    "  + cat\n",
    "    \n",
    "+ val\n",
    "  + dog\n",
    "  + cat\n",
    "+ test\n",
    "  + test\n",
    "  \n",
    "其中使用train中dog和cat各2500张图像组成Val数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data augmentation and normalization for training\n",
    "# Just normalization for validation\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "data_dir = '../dogvscat/'\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val','test']}\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
    "                                             shuffle=True, num_workers=4)\n",
    "              for x in ['train', 'val']}\n",
    "testdataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
    "                                             shuffle=False, num_workers=4)\n",
    "              for x in ['test']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val','test']}\n",
    "class_names = image_datasets['train'].classes\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化一些图片\n",
    "\n",
    "让我们显示一些训练中的图片, 以便了解数据增强.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23581f812b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(inp, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dataloaders['train']))\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs)\n",
    "\n",
    "imshow(out, title=[class_names[x] for x in classes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练模型\n",
    "------------------\n",
    "\n",
    "现在, 让我们写一个通用的函数来训练模型. 这里, 我们将会举例说明:\n",
    "\n",
    "-  调度学习率\n",
    "-  保存最佳的学习模型\n",
    "\n",
    "下面函数中, 参数 ``scheduler`` 是\n",
    "``torch.optim.lr_scheduler`` 中的 LR scheduler 对象.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "                model.train()  # Set model to training mode\n",
    "            else:\n",
    "                model.eval()   # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward\n",
    "                # track history if only in train\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # backward + optimize only if in training phase\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # statistics\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # deep copy the model\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 显示模型的预测结果\n",
    "写一个处理少量图片, 并显示预测结果的通用函数\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_model(model, num_images=6):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 微调卷积网络\n",
    "加载一个预训练的网络, 并重置最后一个全连接层.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ft = models.resnet18(pretrained=True)\n",
    "num_ftrs = model_ft.fc.in_features\n",
    "model_ft.fc = nn.Linear(num_ftrs, 2)\n",
    "\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练和评估\n",
    "使用 GPU 的话, 需要的时间约为70分钟.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-cc88ea5f8bd3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n\u001b[0m\u001b[0;32m      2\u001b[0m                        num_epochs=25)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'train_model' is not defined"
     ]
    }
   ],
   "source": [
    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
    "                       num_epochs=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x178677a0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x178646bb6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x178678f2e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1780af5aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1781cb6e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x178b43a1400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(model_ft)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "卷积网络作为固定的特征提取器\n",
    "----------------------------------\n",
    "\n",
    "这里, 我们固定网络中除最后一层外的所有权重. 为了固定这些参数, 我们需要设置  ``requires_grad == False`` ，然后在``backward()``中就不会计算梯度..\n",
    "\n",
    "你可以在这里阅读更多相关信息\n",
    "\n",
    "http://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_conv = torchvision.models.resnet18(pretrained=True)\n",
    "for param in model_conv.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# Parameters of newly constructed modules have requires_grad=True by default\n",
    "num_ftrs = model_conv.fc.in_features\n",
    "model_conv.fc = nn.Linear(num_ftrs, 2)\n",
    "\n",
    "model_conv = model_conv.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that only parameters of final layer are being optimized as\n",
    "# opoosed to before.\n",
    "optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练和评估\n",
    "在使用 CPU 的情况下, 和前一个方案相比, 这将花费的时间是它的一半. 期望中, 网络的大部分是不需要计算梯度的. 前向传递依然要计算梯度.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/24\n",
      "----------\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-59-029e6a48039b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#仅训练最后一层，时间较训练全部参数下降一半，1080TI约30min\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m model_conv = train_model(model_conv, criterion, optimizer_conv,\n\u001b[1;32m----> 3\u001b[1;33m                          exp_lr_scheduler, num_epochs=25)\n\u001b[0m",
      "\u001b[1;32m<ipython-input-5-77b76ce2b63b>\u001b[0m in \u001b[0;36mtrain_model\u001b[1;34m(model, criterion, optimizer, scheduler, num_epochs)\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m                 \u001b[1;31m# statistics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m                 \u001b[0mrunning_loss\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     44\u001b[0m                 \u001b[0mrunning_corrects\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreds\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model_conv = train_model(model_conv, criterion, optimizer_conv,\n",
    "                         exp_lr_scheduler, num_epochs=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23581f2cf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23589dcad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x235826357f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x235a1958400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2358f18e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GSDEvXVWlIs8zlFLY0q7njkpxd7vWBEtp1z7SkmfJA60VhdL0JxnTUhAEAVrN03hlhdaa/b17vPLVP+TuvWt4vo3tCNJsgrRrnzubzWg2m6yvr7GxscHp6Skry8vEUczOzg6zWQpIlhY3SZKY2SylKEv29veZTmdsbW3hui7tVouyKNHzoGZ9fZ0g8GgkEXmRYokK13VJZ4o0VWDkd7HKB8e5IRI4mz+WRYUQAmk7KKUQiNrvzX1LURbzQKj+jlElRluMpimZtvD94JttotEKev0eu7t3uHnrKkdHBxwe7pPnKePxsL6WlAghSJKEoijY2tpCCMF4MqbVTvD9gDhqUOSa9fV1VlaWGQ2HlGWJ73v0ej1s22ZpaYmFhS5FUWDbNnlRsLNzl8GgVw+lqkTKer5bqRT9gYTw3xvnw0fO/SOiTkxneUZlQhxXYvQIIR2EMRgs0grQCteBMlNUxkEUCsuGvMhJxCJhWKD0MffrkbRW7Ozs8KUv/T7TSc4PfeyHKUub/nCANhpMgNGSL7/ye8SdmMGgxJMRH7nyBLZnUVaaS1eu8NgTT3FycI/Tw2NOTk6YjMcsrkRUpUWZSibTu/QHQ1aWl8kKBw3cuf0WeVmQplPyrCAOAqTSdWwgL1CwI8RcGy+A+XCZVwbfryv9ArCEQBuojAAhEEbXH58vz3BcyHKFqQSVVdWRranno0KYM8VBo9FkZ2eHZ559jjBO8H2fssx54dkfpFQlz7ywgd28zNXr11hbavP2K7/NSus5xpMZtuthqKXuw/EIYVmsrK3wjWt3GR7ssdhq8dIPvMTp6QmOIxlNhyAMZT6iKBVlWaIqhYOECoqixHMfDAXnYmi1pPzmYgdj6opFmt73XiDnxeSyrHAcB8/zUUqfpbh8z6OcTzeseUgvBAhLzEmtmXQch3arxcnJKb7vs7m5Sau5wNVrbyEtxfVrtzge3uXta69iVMrdG6dI6bCxsUXgR2htcTIasbSxzkzN+PhP/DCy8Rj//i/9B3z2r/4op6c91tcuMR6PUXnKtTe/zp2b1ymLgqqqKIsSJGhbY2wIGsG/Zo8/kw0fSCsPGMZoKqXm9caaKNuRqEpRlXUgYVkCozWGOjerTd3riqIm8/3C8vsIw5BGo0mapuwfHDCZjDk6PsayLNqtBRzHJggcBJJ7b3+Fz/7YD1FMZrhyBSk1tm24fGUdTYYjbEa9AYEtkbqkG8PwqMeffO1dpJScjvosLC6iVYlQKdlkfJa/rVSFUhVYBssG6VygoVUrfSbfsG0XdK0CqFSBQaGNqdNaWU5RVRh8pDCAhUahAaXqKLHIxmSWRFoSY0Or2SAMG6yvLVNWCinrxPbh4T6WI3Eci2ayhLA0b7zxBq1WhyX7ca6+eh3bs1h7xOHOvev80EsfZ2N1kaP+iOHxKdvjMaEX8s7r7/DMRoxdOVxee4Hdo21azTa+73LjvTvs7tymNIoiS8nSgixLGUxGTNMUjGI2H3k+LM4FkUVRzascDuh6qLVtG9u2sGSdijPUw6SZ+0phFKrUKK/AES5KGXzPxRIKywpYWGjQbLWIggDHTkDkCEvhOjGjcZ92pw1a0T89BWOxsrJGs9EBBCJUbC0/RhhEjCcp722/g8o0kevx7jvv0T864GQ04PHHHmNxcZE8r4iigKtX38MgaLeaDPtHFGVOWs6wsLC0QhiN0iVplpGmGbYl0Pmfahnkd8S5IDLPcyxLkOc5URTjWBLbrofGegit/ZslLDD1xN/3PISVI6TAaIEqwfMDVlZWcV2/TpVJCXMfa4BOu02ns0phNsjznOPeCDdIGI5GDEcT1lY262SEVDz1kUUqLOKkSbe9xqVH1nFcePbpp3hlNOTKY4+RlhWHpz3Wl1e5ceMaWms67Tbvvfcm6aTHvbu38WyPPE8pqqoWjFkWxtQJfWEERflgMgLngkioNTnSdsizFNexsSwbIcRZBQTqHllW78+7DLa0KZVC2BLPD9ja3ERYFmEQ4fk+juMghEBVMBqNSLOMsiyxgwbt7gpL2qBUxeJCmywrOD09ZXl5hSAJsWyHuNFib/cA329w5comWCXNdsLq+iZFqXBdl6TR4Ctf+hKWBUkccdrrgck5Ptrl+HAf3zJ0FrqgNel0hkCQpRlaa4S0CXz/gdjvXBAZxxGz2WweENQ50sl0gsyseXIcQCPmpCqlcW1J4Ekyreh2l+i0OyBknR2qNJ4RpGmOFAIpbcIwxnU9bNuh0eqyuNAlaTRRWqPyMSf9XfqnQxqtFo888RiDwZDjkz6PXH6M49MBaEWZpRzvn9Lv92m12wgh6Pf7PPf8ixwfHrCze4+9g7vMxvtcv36D0PbRWjGdFthhTqU0eV5iWXVcgCPIiwvUI9vtDlFU1/5msxTbcZhOC0ChDaTFDHeuHg+CgFI7NCxNGAg6y8/SiQOklMRxTFVVSMvCssCVNhiDF3iMRyXgoLSh02mwtLpM0mzhBxG6qHjq2ZcYT0dsb9/m5OAIx3EIXZckcvnqq9c4OVnj1S9/ic21Jzk5OuTxxx4nzVICz2VppcU0H+ENXMZFD1vCU089hRQCV9qoKiNsdZHjjPFgWOeJDQghMdaDKUieCyKllERRjG1nxHFCUeREUUSeF5RlQeB4VFWF0RphaplkjsfapXXwG+giJwpDLMvCEqKuShiDtCyMMRRFLSFptlq0Wi16RycsLSxxcnDEwtIyi91FNjbXuH3nJmW5hslzjDGc9o7I357xwrNPcno65P997W1+9R99gY9/6ifZ3Nrizu3bTJniNTyu3rzGb37ht/jZz/wEx/vbvHnrNabTHqEHoefRrHJOpznCKnGkTVnWArHyAfnIczGPtCyLqqpwXQ8pJUEQ4roujUaCLetKh+95LC0sYrRCCIkTd/GCBMcShGFYVxXmgY2wLHzPIwgCkkaDTqdDWVWMRiO01lRFQTadsbm6hq4UxyfHDEdDWq1WnX1RJa5jU5Y5rVaDJz/yCCsra+zu9/joyz/M5cuXuXr1PSbTSZ2Q380pyoC/9Z/+HRKvwcrSZYrM4dHLL7Kx+gyYEGUEqtTkWTEXmdXF72+d7/5ZcS7WRz7/wnPm/SRzNQ9m1LxMpZVGGIPnuXiux3Q6ZWlpAS09Aj8AU889gzBEWhZlVdFoNBBC4Hle3aZSnByfkjSa/MALL9E/HbO0sowbhCwsreD6HkWV8/LHPsZ7197lX/7WF4jjCNt2eOKJJ+iPTnhy62lKBdv7exRlMVcDTFlYWGCsFb4t6B9t889+7X+j215isbvE3t4B68sbjPp9Tkczbu7scbi7Q5IElGVOo9nCtm1e/dqffOii5Lkg8qM/+JLJ8xzP80jTFGPMGZFVVaG1QgiLJEloNltz4bHA9zwc16WqDK5Xa19d18V1rDPtqxCC3ukpG1uXwZLESZNm0sT3fTqdDgClrvjIM8+RZjmD0QQ1HnBn+w6+7/P444+ztrJEEIYI4LTXY+/0hN3dffKs5LnnXsCLW1x7+xvs796hEzv80e/9c5rNBC/wuLNzh8CLkMJGKUWWZbiux3g8Rdo2w3TGK6+8fjFWY7muizEGz/XI8xzbts98W1HkOI5Lo9HAdV0cx6Ys650swjCsxUx23fuqsqTdbjPon5AkCZZlMZlMsCyL6XRKEMXYjs1sllKWJZPJhMcffxxyTT6d8cabb7G6vsFjj7+E4zeYpj1u37vOtWvXieOIMIzIspTCgla7S7u9gEYyOD7k+HAfnad84Xd/gyef/ghb60t88Xd/B5UOWd1sMugPamFyWSI9F6YTgkZCZyl5IDY8Fz3yxz75CVMURT1xN4Ysy5jNZliWhed5aK1xHAff87EdB9ep/w/en/QLGz+oI1fXdanKjDiOkVKyPS9Ir6yuUyrNE09+BGEsHMeh2WxyZ3ubxXYbIR1WNzZxgxDfT7i0tcEkHXJ35wajkwlKKYLAJ4oTGotd4iihPxhzsH9I/3CXd976BqiUxBf89m//Op/72c+RhDF/8MUvosucZhKRpjO0MVhaARVu4JOVBV/4g3cvRo9M0/RMRV5VFUVRnE3m369kvN/DjAHPq4MipTWe71NVhjzPWVhYmCvMzdkDIYTAKE2WZVy+8ghFUbC0sEye5+zv7+O5LulsRpw0GfUHLEcJhozBpM/G+hZLi+sU6YSqKvF8H0tYVLYFWKSZJm40mZ4e4DqS6zduIaoJP/fpT7Fz+zrGCth84kWO7t1mOBmQZQrHcQgcB9cVZHmGFg8m3jwXUevg9JAym+BK8GxN6FlYlsCSHkHUptls15N7y8b3AhACNdexNptNHNfFaE2v16OqKlqtJRA2rhfUab+kQ7ezxGQ0JbBdUJp8liIRLC8skmY5k8kYowrSwQlCSwbHJ6hiRhQKmt2A7nKLKAkJ4oCyHDMdjAgsFzOZkZaKJApZW21z5coGN3dP6SxvsXX5MoGrWF5dJWl3WV1dJZucEEQ2caPB8vIinrhAq7E2VhsMBn1mRb9W0bkxYdJGaYuqKql0WQdCWUarFSCEII7jWuqYZXieh+u6ZFlGs9lEWg4OkixLcV2XMAwpy5IoirBtm36/TxiG2LbNeDym1WoxHA45OjoiSRKUqoiTqE6jIXBdh7KYJ+6BMGxytLfLZDCm3z8hrKARBZjVDRpWg0uXFK5nc3B4SBg38CKH5176GCdHh6xceRRLKEaDHpbR4F4gXWu3kfD8U0/RCAMWu11sex6V+j62beM4DhiD0YaqLM7miK7rYtu1QMsYw8LCQp2MFoJWszlffOqdqbrjKKqTBla9amo8HjMej5lMJiRJQhiGjEYjer0e7VYb3/fR84R9VVU4jqBSGrQFptYnDEenzMqUkTtmHI+YBjOk5zAcTWi1O8yynEub61RFTmUUCyur+EmXxY3LpNriJ3/mrzwQG56LHqlzTaYzNlc2OR5OKE2G63koDbZto8oKbQxRHCFlfcvv90rLsqiUYWlpiaIo6qhXumRZfqaMi8IWzUaDRqNBr9ejLBV7+/ssdLtnc83RaEQURezt7+MnpxRlztLyIk8//QRFP6fZaDMe13sNvHPtFjffuwVGIQXcnR4xSY7ZN9d4tHmFKPWRrstoOmN1bR1RZUxOT1hbXCTpdJmVisF4TNLosH86fCA2PBdE5lVJ2IgwaGb5EIED2sG1HfJsimu7VEqRZQVBK0JpQaVAaEMU+QSBhyVsAt8hCi2iMKY/PMEYhR+4xFGDNM2pqgFVpcmLjDgKGY1rI5alImkEHJ8OODi8RzJpsdDw8B2HQa9Ah5LBXo+TwyOO9o9I+/vk4xGD8ZBWp01s5Vw/3KMfFtyp9sgaBlvDerxCPtWU0mWqgNGU4TTDC3xi3yc3Grv57Xb//NPjXBAphc2gNyCKA0I/pJwpXNehqjS2bcN8mA2DAG0McRieyfbfX4sRBHVgk2UZg2GP8XiMMYJG3MH3PeI4xnEcRqMRUgqEqH1fOo9sq1JRVYZuZw3b9nD8mNWNLbDA5Jre4QG3rl9jf/ces2EP6Tj4UUBRZPSYImOPfn/IQmeRt3vXCC2XN/evspyssD5c59LaxpmbyLP8LErPsux7G+gD4FwQ+cTTL9Fqx9zd2cZtpBS7R1RFRaEUXuhgVJ3psR0How2qqpjNZoRhvWLYdXxms9nZotPh6BSlKpaXV6iUOctnDofDWhyczvA895vbZKKQlstCZ5koSlhYXiVptXBdn0JVqFJxdHiI0RXj4QlSwNJSl+FkjMZiVEw4HR3zyOYGQks2V7coqhxLTiiKKQhdSzuALM8JvXrfHtu2ieP4u5nmA+NcEPncD3+KPE9Zf+J5Dvducm/3t/ADH1sbpG0QTk3EaDSi2+3CXFCsta7JzKuz9J6UEtsRLCwssNhdRMp60U+v1yfLM05PTwkC7yxAaiQNiqpieXmZpcU1NjYuoWwPYQm8MGA8G5ONcso8ZzzogS4oNVy/eZ1Op0vciMnHM1ytaNo+lh2gjxV5WmBLBzstOOwdIJseiwsLdBsJEkG31WYwGJDn+QOx4bkgctA/YnVlCV3mBI4PxkI6mtkkJ7BjEBbSsihKTVkZsrxOVr9PpuNKZukEx3GYTDOklPhBiBA2rVab4+N9tK5I0yllmWMosSyrnpaoHCxRBxkyAAAOy0lEQVSbWVHgtmK8doglHbQyFHlBkVb0e/sc7u+yt7dHNquH7+7iIpVS5HmBYwcsbz3OcXqEKo/JJhmj/pA4CBn0enziyR8i1AKpFRSG3cNdLAyT8fgsR/xhcS6ITJKE09NTWknESe+UbrfL7uEJUdTCdV0sKYnC8GyITJKEsizP8rKO48y3Yqmlk2VZYsl6N4/d3T1arQghUha6XeI4pqxyTk5O5v5Ssrn1KKtr67SbXVwnBlwm6ZjJeMp0NuHg4IDpbIbv+6BLWq0WRggWFxbwo5BFFTGTM967e0Aqc37+Jz/HO2++iScdAixev/UNrsRXeG7lo1i5YH1xi8FJj1brwQQ6cE6IlLaN7/scHBxgWRZ7e7sEcYswigCJ7TgUZZ0iA/B9n/F4fEaonKvu3k+0t1pNVKWI2xHTacZ0OsX3/XqVcRCQ5dDtdjGm3q+uLKq5RkhSFAWTyRRjCm7duQYobNtmdWWF3ukRzbkaQVNraC3HZnFpkSN1yEp3gZVLy/QODtF5yWA24njvkDjymIzHDII+i54HZUmWzRgOVP1wPACcCyLLouD46IDIc4nCkEcffZRb23tIOQNhszCfLyqlznKwruvWE/95MbgsS9I0JcsyHGcBWzoMR0PqenNJkiTfLOIKjePYFEVJp9PGDxqcHPewnYjJNCPNpvz27/wGb771dX7+53+KZrBGGEVo1SKdjlBKkc5StABjCU7VEJlIVjsLhDl88ct/yKc++SlmkylNN2StucDeG3sM9vfZnd5BCkMzCpg6ztko8mFxLojMR0c0A8l4PCAvClxP4gUa6WgsYZNlKa1mlxLF0uIKk+kpruvSaS8AFkoNAI3jSIpCcHx8zMryGs1Gg8OjU4bDUwCSOK4T7pZPqxkyHPWpqpKbN97j9vZtLj9xhZPeMb2sx5vfeIXx6ZCPv/Ai0ZUmuD62DChVn8loSpqm9Qpl6dLwXVI9pVIzDnsFm+sbRK0Gs6ogasQM8j5+HBCaCCMEw0mf0PXBMmh9gXpknucEQUAYhjWRbr0/XF6MabcbFHMNjed5VErhBRGNpEWz2WZpcZmT0wMcx6GaT0tOTw5xbAcpbVaWl4lCj9F4hOu48w0f6rxsnhd85St/zNtvv87e4T26X+vgRC7FcIIRELg+f/jVL2Esw3PP/CBZlnJ80qNI6+G61Woxnc3QjiEtKrwowHg2VsPm9o2bKAyRkUTNBQ63j0l1juMYWsuL7B7usbHWQZrxA7HhuSByMBwymUwIgoBGo4F0LN56920sAbPZlDAMqaqKJGkC0Op00UpgqHOfQdjAn+tYDw4OWVu1cB0fYyym0wnj+dqLvMhJkhgQ86lLwGQ6xaiUKHCZjHv4KgAqBB55WpAXE7bvXufy1qMURb2a2rKss3xws9FgKiq8BHYnfVzXoT86Ja0KAj/ARjI9OsIvPVxLoIREpzmJ7TObaYzXeCA2PBdETia1iMmZTIiTGDDYroNSEse2yfP8LEoNI4fheIpj+zSli3QDPOmS5RmjyZiiAktVVOUM34/I5pFtlmX0+30GgwFhmNBsxFy/ebVuO1e4tosrPILARa77TE8UapxxtL+N50p293fw3AQQZ5UT3/dJ0wwZJmTZGCrJ7sEOrUaTdFRLV8bHPaS7yO7I5d2hywCPSjaJq5Sn1AnPRYcPxIbngsh2q0Exl17Uw6hmc3Wd/YMBuqyQno3SBWWVEfgttFFYwjAYnGLbFnErYXv3JlU2xRICYUvKvKDdUpQ6PwsoPM+rt+rUM9JckIQeZXZIs1n7qTiOWVpaYrJS8c7sGlmew0wymUyYTGYsbm1gWRpTStL0lCxLycSUphtQpTk3b1ylsRAwHRxxZeMpxsWIvGpz98Ya40EICpxGQlvv8/nFMbJZMAzcB2LDc0FkEASMxmOklEynUxzHptlsMptpJpN6M4iyLLFtm8lkShB6ZEVGO4pxXZfxaMDd2zc53N9la30NIQ2W9GjECa4foMjOwvwkSVCiwrUcOp0OLzz//DzPWp0N7Zmc0gt77E9OmKoZ0+mM69fexHM9FrpdjJBYlkVZ5iA0VlqSBG0eXXue1979Cu21Fkk5Y3yyzVLV5Ilnb3MldNj+l28Q3MkRM8N+IjA/+ix3xg9GDnkuiCyL8qz4K6VhNpuQVwWT6RRpx0ymEzqdelPeRlLXGW03xBhDHMe89gevMOyd4NuSPJuxt3ODZmeFS5uXqTQEvo8tbUbjEY7tUJYFlm3huB5JkoARZ3uWG2OIVMBWdx2VGXaHM06OeyTJPaTt8Omf+CxK1PPPLJ8BioNyQixDbh3u4CeKpUzzJDPChSkr8ZTD1zzGN2ZsFT6OapIz4JFpCr9zg/QHPtifvvpeOB9EViXGmFpMZdm4rs3uwV4taTSy3tPGcfBcD2N0vS1K3MR2PWazGaNBj9Bz6XaX2b5zi3wyZG+akz73UdqLaxTjIXv7+7iuy2Q6IatSIj/EtV0azQZGcTYXrbU+mnbcoWhlTO4dMnI1u7u3qKqCuzvP8+ilx8iyrN7qM/KJioJkCj+xucpCnOL4I0R+Cz+r+MY/65H3QkKhsJWiUjkdJ2ZcGLqeYf292w/EhudCIQDg2Ha9UqnKUcLC8ZusrK6RV1OQkLQb2J6DEoYCwWSaofKSg7vbdWJgaRkvsFlu2zSbEba0mQxHBFYttwwjD2FVOF6tyHMdB8dxcCwbKQWuaxNFAWHoE3cbdDsdlpdWiFsd8rzCaI/xKGXv4AYzrRGeoTIzhBLYvsK1BG1nzKKe8qT3BE2GTHYr3GmIq3KCwqJrJaw5XaTrsxlEtFSJKC9Q0nwynZLMyzmedLAtjW07XHnkSRZXLrF3eIC0fTqtBZrNFrbnEIcev/mFf4HnBGxc2uDFj/4gk+EJb2cz/FnB9sEtrt94l/W1TbQB1/VJU006yxG2w3gyIVnqMgNsSyKkXS+qVZq8qrACF9lt88ilTQo95fRkxGhwyjdee5215Su4jsVCZ5HpOMfx1qmqgpX1JZbkDXbTNymGHQ5ez4hTl8eER+S7nAxOGPs2sfbJg5DpM8vYyxdo413HqQ1r2zZFnuPYDq7tsbyyxSNPdfgBXXGwf0wYRMRRk6QZcef26yDqlcIfefxJOgsrtLoLrK+t8e47r3Nze5uT030GgxFx0qARt+papi4pVDXPy5bYrkOVF2itKVUtx6w3nSixBKyvreAEFtvb96hKjeO4DE7v0W4t4rshyofBsE+ysk5vdIIbttDVLr33NNFMUnFCGndQjQj7kUX8TpNUaA5sxa4qaTsXKLPjznOOVVXRjCKyLCfwYxw3pNleJgp9ti5/BKMERVmRz8a88dZrrK63+dGXP0m7vUjS6CIcSafxKHYU8fLOu7z6lddAG1zHww888ryg1z9BiQrcWlA1S1OEMZTzTSaUUkihsa16pVS33cT1GuTTguPTPYTV5/DuO3Tiv4TjNUnzU2JngmU/x/HkcRbWnuHyWofBzXfQznvYn3I4yS3CVkxpS3RmCGxY9gOiYYnSD0YhcC58ZFHW9UHHcZhUGYW0aHQXcXwfo0ocKbEtgSUgm004ONkmsBULscfR7h0KbKSlib1a5xOKEKk9pPRJywInsNFCU+gKpF3vGef61H+KwK2/E9Q+NIptokjguOD7Lo2kSaPtsbiwSuw00PmMWXUXEVWM8pSqKCnymNHkHrN0zLW7Fctbf5mf+c/+I1Z+6mdwly+zuLqCjUsgbAK7RFUprm0xy04YT3oPxIbngsg4jM56pbQsbNvB9wOSOMHzvflK35TxtM/RyT12bl9HagujBf3xgNf/5PdQ2QBPaI52DxkNduj37tJsSOLIzPcosAjnmwC6TgMLhzgKkHZFqTSu6xP6Ma7jUVkJftLFTzoMswJtHFY21llaX2cyg489/xkip42uCgb9U/bu3eX6tXcp8hQp4L133sX3Yn7805/jxY/+OMYL8WKXJAyRMqHRWq43CZaSTrv9vQ30AXAuhlbHdahUhe95ZFW9vn5hYQHf9zG63hWrUhVVmdLvH9A73CHxQ2zbQTs2nm/4xte/jOfEDAcpg+E2nXbMeHDK8dE9pNfG8zymsxlCCBxbUuqCqvKwhIXr+ShVIS0HWwosy0ZXGmmBbQkcaWP78OjlDV5+8ZfYvnqT3BmTZxkIiMOQbDbj9PiIvMgYjmZsrK+yurzEsy98HE3JK7/76yw1ElrdBU6OjhE6JQgCpHWBEgL10jlNWVW1r5qLolzPww8i8qIkz6acnO7z1ttfp+1oXJmgERQqx84E4/GEkdFY0sIWDhKJa/v0T/pcetStRc7AeDxGuilJsIhWks/89L/Nzq33uH37JkqXCGEIDbS6LY6PDkCnmAJmeZ8qS1FI1pdcjvITFpcWeebpT2Opii/85m8SRj6TyQiskoPD2zSaAUljkUtPfoyj7VuMD64xS0+wXIOtHdI0Pdto+MPiXBApXQ9dVmih8RwPYUesbjyK63q4jsaSLtOZw7tvvMGyb+N6DsK2sIXAyQyW4+C48kxlLswQW4Yo4dJPT8gqi2cuP0rS7vDEk4+x2KmLzEZriiLH6GfIyyl3r72FJyyMypkxRaiMRugzGY5whYtwLLJqTK4qVtorZEpQpSfIqEHc6ZAVhma7QcNv8/TjzxPGbQwWtu1zfHhMgoXW1FLOoiAtKkbjB7Nk4FwQ6TgO3U4HYwx5Oqn/kJm0iJMYz4W00DSb8XzYE1RldbY1p5zvx5NnOdKul3R7YQensgijBq4q+Lkf+wSbl5bIyzFLiw1G4wKFhfECvKiN48S880bKqLdfb7XtReSzKWEUkuUZEkMQhfQHA4osxU1iwsBGTmeI7AjLFfzCZ3+KmXZ58+ZNfuHf+TyFSrEriSVtXnvlizRij2JoMAIO9w9xpM3m1tZczvLhcS7WRz7Eh8e5iFof4sPjIZEXBA+JvCB4SOQFwUMiLwgeEnlB8JDIC4KHRF4QPCTyguAhkRcED4m8IHhI5AXBQyIvCB4SeUHwkMgLgodEXhA8JPKC4CGRFwQPibwgeEjkBcFDIi8IHhJ5QfCQyAuC/w/MAA2gPaSuTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x235a19e52b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(model_conv)\n",
    "\n",
    "plt.ioff()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试集预测及提交"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = []\n",
    "for i, (inputs, labels) in enumerate(testdataloaders['test']):\n",
    "    inputs = inputs.to(device)\n",
    "    labels = labels.to(device)\n",
    "\n",
    "    outputs = model_conv(inputs)\n",
    "    preds.append(torch.max(outputs, 1)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model_conv, 'dogsvscat.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=[]\n",
    "for i in range(len(preds)):\n",
    "    labels.extend(preds[i].cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "fileindex = []\n",
    "for i in range(12500):\n",
    "    fileindex.append(testdataloaders['test'].dataset.imgs[i][0].split('\\\\')[-1].split('.')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.Series(labels,index=fileindex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('submit.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
